Computer method and system for ranking users in a network community of users

ABSTRACT

A computer method and system for ranking computer network portal users is provided. The system and method comprise a ranking module which determines rank of an individual in a community of global computer network users. The ranking module determines rank of an individual as a function of user demand. The function of user demand includes any one or combination of number of requests to be connected to the individual user, readership following of the individual user and keywords common between profiles or authored works by the individual user and those of other users. An output member is coupled to receive the determined rank from the ranking module and generates an ordered list of user names ordered by determined rank of individuals. The rank of individuals may be provided to external entities such as fraud detection systems or advertising targeting engines.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/986,796, filed on Nov. 9, 2007. The entire teachings of the aboveapplication(s) are incorporated herein by reference.

BACKGROUND OF THE INVENTION

A global computer network, such as the Internet, is generally formed ofa plurality of computer networks, servers and end user computersinterconnected across communication lines. The computer networks areloosely coupled for communication to each other to enable access fromone digital processor of one network to a digital processor of anothernetwork in the plurality of computer networks. The servers provide toend user computers access to the computer networks and to the variousdigital processors in the plurality of networks in the global computernetwork.

Publishing and viewing information on the global computer networktypically requires two software components. Electronic publishers run,for example, Hyper Text Transfer Protocol (HTTP) server software, whileusers scanning or searching on the global computer network run browsersoftware. In the example of the Internet, the World Wide Web (Web) isthe protocol used to create and publish documents. Web pages displayinformation, point to other Websites or provide a user-interactiveapplication (generally referred to as a web portal). Some Websites andWeb portals provide commerce, e.g., retail sales, secondary markettransactions (E-Bay), or other trading, while other Websites servegovernmental, philanthropic, social, or other purposes.

There is a growing number of so-called “social networks” on a globalcomputer network. There is a growing acceptance and use of the same.Examples are GATHER®, MYSPACE, FACEBOOK, FRIENDSTER, and others.

In order for an end user to take advantage of this wealth of informationand activity offered by a global computer network (the Internet), oneneeds improved tools or systems for searching and navigating the largepools of databases and electronic media of such a network. Improvementsin ranking search results, profiling features, and orderingrecommendations are also needed.

SUMMARY OF THE INVENTION

Embodiments of the present invention address the disadvantages of theprior art. In particular, an embodiment of the present inventionprovides a computer-based method and system for ranking an individualuser among a network community of users. Said another way, an embodimentof the present invention determines the social importance of a user (ordemand for the user) relative to the network community of users. Thissocial importance is measured based on demand of others to be connectedto a subject user, levels of activity (subject user interaction) withinthe network community portal, and/or readership following of the subjectuser in the network community. Other metrics such as popularity, socialactivity-type factors, and the like are also suitable.

In a preferred embodiment, the invention method and system identifiesimportance of an individual user, in a community of global computernetwork users, as a function of user demand. The invention method/systemnext determines ranking of the individual based on the identifiedimportance. In a preferred embodiment, this ranking determination isrecursive using rankings of people connecting to the individual.

In some embodiments, the ranking enables earlier computerized discoveryof online information based on the browsing activities of highly rankedindividuals (experts). Experts may be identified to do any one or moreof creating content, commenting on proposals, participating indiscussion, and reviewing publications; where the identifying is basedon any one or more of user rank, profile characteristics, and areas ofinterest. Experts may also be identified in a specific location.

An output component provides an indication of the determined ranking ofthe importance of the subject individual. The ranking may be visible asa number, a tier level demonstrating status, or both. In someembodiments, the determined ranking is provided to targeted advertisingengines, fraud detection systems, or external entities such as othersocial networks.

In one embodiment, the function of demand for an individual userincludes any one or combination of the following:

-   -   number of requests to be connected to the individual user;    -   number of actual connections made to the individual user;    -   number of messages (e.g., email, text messages, and the like)        sent to the individual user;    -   number of messages (e.g., email, text messages, and the like)        sent by the individual user and opened by other users;    -   number of public comments made on a profile of the individual        user;    -   number of requests to view content authored by the individual        user;    -   number of comments on the individual user's content;    -   number of invitations sent to the individual user for any one or        more of: i) membership to the community, ii) membership to        groups, and ii) events;    -   number of chat or instant message requests made to the        individual user;    -   number of chat or instant message sessions engaged in by the        individual user;    -   number of videoconference requests made to the individual user;    -   number of videoconference sessions engaged in by the individual        user;    -   number of calls attempted to the individual user;    -   number of calls received by the individual user;    -   community members following activities of the individual user        activity within the network (e.g., number of feed requests made        by other users involving the individual user); and    -   keywords common between any one or combination of a profile and        content created by a user and any one or combination of a        profile and content of the individual user.

The community members' following may be topic specific (by tag orkeyword) resulting in different ranks for each user for differenttopical areas. The function of user demand may be weighted by user toyield a demand distribution for an individual user across other users.

An embodiment of the invention decreases the rank of users engaging inbehavior that is prohibited or discouraged within the community. A watchlist of such individuals and their connections enables systemadministrators to monitor and limit such activities. Another embodimentdecreases the rank of individuals whose connection requests sent toother people are declined or ignored.

Search results for members or content may be sorted using user rankingas a factor. Members may opt not to see indications of users below acertain rank in their search results. In this context, “below a certainrank” may correspond to an absolute or a relative rank threshold. Forexample, users may wish not to view indications (e.g., name or profileinformation) of users that are below rank 1000 or that are ranked in thelowest quartile. Another way to find relevant information quickly is totrack browsing activities of highly ranked users.

In some embodiments, content browsing information of or for users iscollected; and search results for content are organized (prioritized)using a function of ranks of users browsing the content. In otherembodiments, content or Internet pages recommended by (browsed by) usersmay be collected and organized (e.g., prioritized or ordered) using afunction of ranks of users making the recommendations (browsing thecontent/pages). In this way, embodiments provide a method or system ofassembling an improved web (global computer network) search database ofvarious (wide spread) source content. User rank may also be used toidentify candidates to receive a promotional sample or discount.

Users may gain or lose privileges depending on their rank. They may alsobe compensated for participation or publishing in the community based onrank.

New members are assigned a rank of users who invited them to join. Newmembers who join without being invited are assigned a different initialrank than those who are invited.

An embodiment of the invention is an advertising targeting engine.Advertisements that will be displayed to a user may be chosen based onone or more of user rank, profile characteristics, areas of interest,and geographic location. An advertising pricing system pricesadvertisements based on the rank distribution of the target audience.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIGS. 1a and 1b are schematic diagrams of a computer network and a blockdiagram of a computer node on the network, respectively, in whichembodiments of the present invention are implemented.

FIG. 2 is a schematic illustration of a connection graph representingpeople connections in a social network.

FIG. 3 is a flow diagram of a people ranking process embodying thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

Overview

PeopleRank of embodiments of the present invention is a system thatquantitatively (e.g., mathematically) produces a measure of socialstatus within large social networks of interconnected people (generallycomputer network communities of users). In most social networks of art,people connect to other people. People issue connection requests toother people they have found online with whom they wish to connect. Thisproduces a directional link or directional connection from one person toanother. The direction of the link is from the person requesting theconnection to the person being connected to. This produces a large scaleinterconnected network or graph where the nodes in this network arepeople and the directional lines connecting people are connections.

PeopleRank takes into account not only the quantity of connect requests,but also who the person is that requested the connection. Moreprecisely, the popularity of a person is proportional to the popularityof the people who have requested to be connected with that person. Thepopularity of the people wishing to be connected to that person isproportional to the popularity of the people that have requestedconnections to them, and so on. The term “popularity” is used below inthe sense of importance (e.g., demand).

PeopleRank measures the importance of individuals in a social network asa function of demand for those individuals. In a preferred embodiment,this measure of importance is derived recursively for the entireinterconnected graph of directed connections. The term “PeopleRank” mayrefer variously to the invention system for measuring importance, thescore of the importance of a particular user, or the user ranking basedthereon.

In this manner, receiving connection requests from highly important(in-demand) people is more valuable for one's importance ranking thanreceiving connection requests from people who are not important. Inaddition, receiving connection requests from lots of low-importancepeople is also beneficial for one's importance ranking.

Mathematical Description

PeopleRank uses the large interconnected graph of connections betweenpeople in a computer-based social network to determine a mathematicalscore for each individual in the network. It does this by mathematicallytaking into account the entire connected graph of people all at once.The connection graph may have numerous circular connections, dead ends,and isolated networks, all of which must be dealt with mathematically.

The connection graph is modeled as an edge-weighted directed graph. Theedge weighted directed graph is a common mathematical problem that haslong-standing computational methods such as power iterations andprincipal eigenvector computation in linear algebra.

Power Iteration

Applicants model the problem by defining a node adjacency matrix E ofthe directed connection graph. E[i,j]=1 if the person i has adirectional connection to the person j and is zero otherwise. Each nodein the connection graph has a popularity vector p[v] associated with itwhich is represented as p.

Using this notation, applicants represent a new popularity vector p′which sums the popularity of linked nodes as a matrix equation:p′=E ^(T) p

This is also expressed as:p′[v]=Σ _(u) E ^(T) [u,v]p[u]p′[v]=Σ _(u) E[v,u]p[u]

This gives a recursive equation which one can use a power iteration tosolve. Start with a unary vector where p=(1, 1, 1, 1 . . . 1)^(T) anduse an iterative assignment p=E^(T) p. Within each iteration, normalizethe vector p to 1. After many iterations, the vector p converges to avalue, which is known as the principal eigenvector of the matrix E^(T).

PageRank

Applicants solve the problem of computing popularity using the followingPageRank algorithm and power iterations. In this model, applicants use a“random walk” approach as outlined in PageRank and apply that to theconnection graph of people.

Suppose a person were to browse randomly from person to person using thedirected connection graph described above. At each person/node thebrowser randomly chooses to browse to one of the directed connections.Applicants model an adjacency matrix E of the directed connection graphas described above. E[u,v]=1 if the person u has a directionalconnection to the person v and is zero otherwise.

Given E, the out-degrees of node i is:

$N_{u} = {\sum\limits_{v}{E\left\lbrack {u,v} \right\rbrack}}$This is simply the count of the number of directed connections from nodeu.

Next normalize all row sums to 1:L[u,v]=E[u,v]/N _(u)The probability of getting to node v when one is present on node u isthen:

${p_{1}\lbrack v\rbrack} = {\sum\limits_{u}{{L\left\lbrack {u,v} \right\rbrack}{p_{0}\lbrack u\rbrack}}}$which can be written as:p ₁ =L ^(T) p ₀

This is the identical form of the equation described in the abovesection and can be solved using a power iteration.

Embodiments of the present invention iterate a sufficient number ofiterations until the relative rank ordering of the vector p is attained.The popularity of node u is p[u], which is referred to herein as thePeopleRank.

PeopleRank Computational Example

Consider the connection graph of four nodes 11, 12, 13, 14, shown inFIG. 2. Each of the nodes 11, 12, 13, 14 in this example represents aperson (user) within a social network (computer network community suchas at 51 in FIG. 1). Node 11 issues connections requests 21, 22, 23 tonodes (connects with) 12, 13 and 14. Node 12 issues connection requests24 to node 14. Node 13 issues connection request 25 to node 14. Node 14issues connection requests 27, 29 to nodes 11 and 12, respectively.From this graph, the E[u,v] matrix is determined as follows:

$\mspace{11mu}\begin{matrix}{{Node},{u = 11}} & 0 & 1 & 1 & 1 \\{{Node},{u = 12}} & 0 & 0 & 0 & 1 \\{{Node},{u = 13}} & 0 & 0 & 0 & 1 \\{{Node},{u = 14}} & 1 & 1 & 0 & 0\end{matrix}$

Next, compute the Nu vector as the count of outgoing connections (21,22, 23 . . . , etc.) for each node 11, 12, 13, 14:

$\begin{matrix}{\mspace{11mu}\begin{matrix}{{Node},{u = 11}} & 0 & 1 & 1 & 3 \\{{Node},{u = 12}} & 0 & 0 & 1 & 1 \\{{Node},{u = 13}} & 0 & 0 & 1 & 1 \\{{Node},{u = 14}} & 1 & 1 & 0 & 2\end{matrix}} & {Nu}\end{matrix}$

Next, normalize the E matrix by the Nu values and generate the L[u,v]matrix:

$\mspace{11mu}\begin{matrix}{{Node},{u = 11}} & 0 & {.33} & {.33} & {.33} \\{{Node},{u = 12}} & 0 & 0 & 0 & 1 \\{{Node},{u = 13}} & 0 & 0 & 0 & 1 \\{{Node},{u = 14}} & {.5} & {.5} & 0 & 0\end{matrix}$Transposing this matrix provides:

$\mspace{11mu}\begin{matrix}{L\left\lbrack {u,v} \right\rbrack}^{T} & 0 & 0 & 0 & {.5} \\\; & {.33} & 0 & 0 & {.5} \\\; & {.33} & 0 & 0 & 0 \\\; & {.33} & 1 & 1 & 0\end{matrix}$Now apply a power iteration with the matrix equation:p ₁ =L ^(T) p ₀For initial values of p, the present invention chooses 1/N, where N isthe total number of nodes 11, 12, 13, 14:

$\begin{matrix}{{p\; 0} = {.25}} \\{.25} \\{.25} \\{.25}\end{matrix}$

Now compute the first iteration of p:

$\begin{matrix}{{p\; 1} = {.25}} & & {0} & {0} & {0} & {.5} \\{.25} & {*} & {.33} & {0} & {0} & {.5} \\{.25} & & {.33} & {0} & {0} & {0} \\{.25} & & {.33} & {1} & {1} & {0}\end{matrix}$ $\begin{matrix}{{p\; 1} = {.125}} \\{.208} \\{.0825} \\{.5825}\end{matrix}$Taking the absolute magnitude of p1, one has:

$\begin{matrix}{{p_{1}} = {\sqrt{p_{1}^{T}p_{1}} = {.1964}}} \\{.3268} \\{.1296} \\{.9153}\end{matrix}$After one pass of the iterations, the PeopleRank for each of the fornodes 11, 12, 13, 14 is defined.

One can now keep iterating by using the value of p1 to compute p2, p3,etc. Iterating can continue until one reaches a convergence to values ofp within some epsilon error or until one reaches a convergence of therelative rankings of the values for p.

In an embodiment of the invention, an output member i) is coupled toreceive the determined rank from the ranking module and ii) generates anordered list of user names ordered by determined rank of individuals.The ordered list, as well as the ranks of individual users, may bepassed to entities external from the community (e.g., othercommunities).

Example applications of the above invention PeopleRank include searchengine ordering a list of people names for display of search results,recommendation engine ordering a list of people names, and other portaloperations ordering results lists. U.S. patent application Ser. Nos.11/371,462; 11/451,995; and 11/593,864 (herein incorporated byreference) describe a network community portal 51 with such operations.The description below summarizes such a network community portal 51employing the present invention People Rank.

Embodiments of the invention can determine absolute as well as relativeuser demand. For example, user demand (e.g., as determined by number ofconnection requests) can be weighted by user to yield a demanddistribution for an individual user across other users.

It is understood that instead of requests for connection as in the abovedescription of PeopleRank, requests to interact or otherwise have anassociation with (directly or indirectly) an individual user may be usedto determine importance of or demand for the individual user beingranked. Demand for an individual user may be demonstrated by any or moreof the following:

-   -   number of actual connections made to the individual user;    -   number of messages (e.g., email, text messages, and the like)        sent to the individual user;    -   number of messages (e.g., email, text messages, and the like)        sent by the individual user and opened by other users;    -   number of public comments on the individual user's profile;    -   number of requests to view content authored by the individual        user;    -   number of comments made on the individual user's content;    -   number of invitations sent to the individual user for any one or        combination of: i) membership to the community, ii) membership        to groups, and ii) events;    -   number of chat or instant message requests made to the        individual user;    -   number of chat or instant message sessions engaged in by the        individual user;    -   number of videoconference requests made to the individual user;    -   number of videoconference sessions engaged in by the individual        user;    -   number of calls attempted to the individual user;    -   number of calls received by the individual user;    -   community members following the individual user's activity        within the network; and    -   keywords common between any one or combination of a profile and        content created by a user and any one or combination of the        profiles and content of the individual user.

These factors (social activities and other factors) can all beincorporated in graph-theoretic terms as described above to recursivelydetermine importance.

Community members may follow a user's activity as described above byincluding the user on feeds (which indicate recent activities of theuser) or by requesting offsite notification of the user's activities viaemail, text messages, or similar messages. These forms of following auser's activity may result in different ranks for the user in differenttopic areas.

Invitations sent to a user for membership may affect the user'simportance both before and after the user joins the network. A user whois already a member of a community may continue to receive requests tojoin the same community, which are converted to connection requests.

Turning to FIG. 1A, illustrated is a computer network or similar digitalprocessing environment in which embodiments of the present invention(People Rank) may be implemented in a search engine, recommendationengine, social network, and so forth.

Client computer(s)/devices 50 and server computer(s) 60 provideprocessing, storage, and input/output devices executing applicationprograms and the like. Client computer(s)/devices 50 can also be linkedthrough communications network 70 to other computing devices, includingother client devices/processes 50 and server computer(s) 60.Communications network 70 can be part of a remote access network, aglobal network (e.g., the Internet), a worldwide collection ofcomputers, Local area or Wide area networks, and gateways that currentlyuse respective protocols (TCP/IP, Bluetooth, etc.) to communicate withone another. Other electronic device/computer network architectures aresuitable.

FIG. 1B is a diagram of the internal structure of a computer (e.g.,client processor/device 50 or server computers 60) in the computersystem of FIG. 1A. Each computer 50, 60 contains system bus 79, where abus is a set of hardware lines used for data transfer among thecomponents of a computer or processing system. Bus 79 is essentially ashared conduit that connects different elements of a computer system(e.g., processor, disk storage, memory, input/output ports, networkports, etc.) that enables the transfer of information between theelements. Attached to system bus 79 is I/O device interface 82 forconnecting various input and output devices (e.g., keyboard, mouse,displays, printers, speakers, etc.) to the computer 50, 60. Networkinterface 86 allows the computer to connect to various other devicesattached to a network (e.g., network 70 of FIG. 1A). Memory 90 providesvolatile storage for computer software instructions 92 and data 94 usedto implement an embodiment of the present invention (e.g., People Rankcode 63 detailed above). Disk storage 95 provides non-volatile storagefor computer software instructions 92 and data 94 used to implement anembodiment of the present invention. Central processor unit 84 is alsoattached to system bus 79 and provides for the execution of computerinstructions.

In one embodiment, the processor routines 92 and data 94 are a computerprogram product (generally referenced 92), including a computer readablemedium (e.g., a removable storage medium such as one or more DVD-ROM's,CD-ROM's, diskettes, tapes, etc.) that provides at least a portion ofthe software instructions for the invention system. Computer programproduct 92 can be installed by any suitable software installationprocedure, as is well known in the art. In another embodiment, at leasta portion of the software instructions may also be downloaded over acable, communication and/or wireless connection. In other embodiments,the invention programs are a computer program propagated signal product107 embodied on a propagated signal on a propagation medium (e.g., aradio wave, an infrared wave, a laser wave, a sound wave, or anelectrical wave propagated over a global network such as the Internet,or other network(s)). Such carrier medium or signals provide at least aportion of the software instructions for the present inventionroutines/program 92.

In alternate embodiments, the propagated signal is an analog carrierwave or digital signal carried on the propagated medium. For example,the propagated signal may be a digitized signal propagated over a globalnetwork (e.g., the Internet), a telecommunications network, or othernetwork. In one embodiment, the propagated signal is a signal that istransmitted over the propagation medium over a period of time, such asthe instructions for a software application sent in packets over anetwork over a period of milliseconds, seconds, minutes, or longer. Inanother embodiment, the computer readable medium of computer programproduct 92 is a propagation medium that the computer system 50 mayreceive and read, such as by receiving the propagation medium andidentifying a propagated signal embodied in the propagation medium, asdescribed above for computer program propagated signal product.

Generally speaking, the term “carrier medium” or transient carrierencompasses the foregoing transient signals, propagated signals,propagated medium, storage medium and the like. Further it is understoodthat the present invention may be implemented in a variety of computerarchitectures. The computer network of FIGS. 1A and 1B are for purposesof illustration and not limitation of the present invention.

In particular, server(s) 60 provides and maintains a portal 51 thatdefines a community of users. The portal 51 enables community members toorganize groups, communicate with one another (on an individual or groupbasis), network with one another (including defining connections betweenpeople), publish authored works (content), and the like. Those whocreate authored contents are referred to as authors or author-users; andthose who view such contents are reader-users. Community members orcommunity users are those global computer network users who interactwith portal 51 (or network community site 51).

The subject system 61 enables members to build a social network. Arelation module is coupled with the subject system to measure the extentand strength of the connections between users. The term “degrees ofseparation” is used to describe the number of users connecting a givenuser to a subject target person. For any given user, the subject system61 shows the “shortest path” linking that user to a subject targetperson. In cases in which there are more than three degrees ofseparation between two users, the system indicates that the users arenot closely connected. In measuring the strength of the connectionbetween two users, the relation module weighs a combination of factorssuch as the number of times the users have communicated through email orinstant messaging, and the time elapsed from when an email is receivedto when it is opened and replied to between the users.

The relation module allows an arbitrary group to define relations amonga subset of users. The groups may be public or personal. Personal groupsare designed such that the users are members of another user's personalcontact and hence have no knowledge of their membership in the group.Members of public groups, however, need to confirm their membershipbefore they can join the group. Public group members are allowed topublish their authored works into a central location for the entiregroup to access. Members are allowed to comment on the authored worksand are informed when a new publication occurs or someone posts acomment on an article that is part of the group. Membership of sharedgroups is generally viewable by all group members.

The creator of a group may disable the ability for group members topublish to the group in order to control group content. In such cases,the creator of the group may have approval authority to approve authoredworks submitted to the group before such authored works are publishedfor viewing by group members. The creator of the group may also makeauthored works available for non-member viewing. Members of the groupmay invite non-members to join the group and the group creator may setpreferences allowing users who are non-members to apply for groupmembership. Groups are searchable by their names, tags or keywords,authored works of the group, number of members, publishing rate of thegroup, and commenting rate.

Reader-expressed rankings are employed to determine the quality orpopularity of an authored work. The change in the ranking of an authoredwork may be based on quality rating of the author, popularity rating ofthe author, and/or popularity rating multiplied by quality rating.Rating of an authored work is done across the system contents withinspecific tags or taxonomical categories. The author-user and/or his workis rated for popularity based on the number of times the work isforwarded or bookmarked, on the popularity rating from users, onindividual user traffic, on relative traffic with respect to otherauthored work similarly categorized, and/or on relative traffic againstother authored work over the entire network community site (subjectsystem 61).

In an embodiment of the invention, a member's rank is used incompensating that member for community participation or publishing. Forexample, highly important or popular people may be compensated more thanother individuals for creating content.

In another embodiment, content appearing in search results is sortedusing the PeopleRank of authors of the content as one factor (otherfactors may be used as well). In this way, spam is not given equalweight as high-quality content in search results, since spammers arelikely to have low PeopleRanks. In another embodiment, the results ofsearching for members of a community are sorted using PeopleRank as onefactor. Users may also opt not to see, in search results, users below acertain rank, and members' ranks may be visible in another embodiment aseither a number or within a tier demonstrating status (e.g., new member,intermediate, expert). These factors make it less likely that spammersor other objectionable people will have their profiles and/or contentdisplayed in response to searches.

Authored works are given a system-wide ranking based on a combinationof: rating distinguishing between serious and humorous subject matter,rating on writing quality, rating on presentation, rating distinguishingbetween conservative and liberal points of view, rating for suitabilityfor different audiences and age groups, rating for compliance with oneor more standards, and rating to indicate how accurately the authoredwork was categorized or tagged.

Reader-users further organize the authored works by suggestingalternative taxonomical categorizations and/or alternative keywords. Thesubject system also organizes or ranks authors by the number of times anauthor's works are viewed by reader-users, by quality and/or popularityratings, and by comments by others on the author. Such author rankingmay be across a certain time period, by tag, or across all tags.

The subject system 61 can also generate a profile of individual networkcommunity users based on measuring keywords and/or word combinationscommonly used by author-users or read by reader-users. Based on thisprofile, the system may generate a list of suggested readings ofauthored works viewed by one or more users having a similar profile.

In an embodiment of the invention, negative behavior is identified usinga user-recursive factor. In this context, negative behavior refers toprohibited or discouraged activities including spamming, harassing,violating terms of service, publishing pornography, using automated‘bots’ to increase page views, creating multiple inter-linked profiles,and attempting to defraud or abuse the system in any other way. In anembodiment, a technique called “cookie proxying” uses cookies toidentify a single user who has multiple profiles Cookies are parcels oftext sent by a server to a client such as a web browser and then sent bythe client back to the server on subsequent accesses of the server.Cookie proxying uses cookies to track browsing activities and determinewhen a single computer is used in multiple logon sessions with differentprofiles.

Negative behavior may be identified based on geography, e.g.,individuals from a certain country or IP address range. In someembodiments, the PeopleRank of an “abuser” (individual who conductsfraudulent activities) is decreased. In other embodiments, a separate“Abuser” score is maintained. In an embodiment, activities of abusersand individuals connected to abusers may be monitored via a “watchlist.” This watch list is maintained with the names of those withPeopleRanks below a certain threshold (or abuser scores of a certainlevel). In other embodiments, PeopleRank is provided as an input to afraud detection system. PeopleRanks of individuals in a given onlinesocial community (or computer network community) may be used by frauddetection systems monitoring that community, or they may be sent tofraud detection systems of other communities.

A user's PeopleRank is decreased in an embodiment of the invention whenthe user: i) invites others to connect and those invitations aredeclined or ignored; ii) is disconnected by another user; or iii)attempts to comment on another user's profile and is refused or has suchcomments deleted. People with low PeopleRanks or who are on the watchlist may have their behavior observed and their privileges or technicalfunctionality limited by system administrators. Conversely, people witha high PeopleRank or in a high PeopleRank status tier may earnadditional privileges or technical functionality.

In an embodiment, new members to a social network are assigned a rankingupon sign-up based on the rank of the person or people inviting them. Inanother embodiment, new members who register (join a community) withoutbeing invited are assigned a different initial rank than those who wereinvited to join. In this way, new members are presumed to have a certainlevel of importance upon entrance to the community.

In an embodiment of the invention, an “engagement campaign” is used toencourage members of an active community to act as conscious advocatesfor a product or activity. In an engagement campaign, selectedindividuals: i) are invited to try a product; ii) receive an invitationto a product; iii) receive a sample or preview of a product; or iv)receive preferred status or pricing for a product. In this context,“product” may refer to a product, service, or event. User rank is usedto select such individuals based on any one or more of the following: i)areas of expertise; ii) areas of interest; and iii) profilecharacteristics.

These selected individuals will typically try the product or event andcomment on their experiences, and their comments may encourageindividuals connected to the select few to similarly try the product orevent. In this way, a large customer base may be tapped by firsttargeting an influential (high people rank) group. Engagement campaignsrepresent a new source of monetization for social networks.

In an embodiment of the invention, users recommend content or Internetpages to their friends and family, and the recommendations are collectedand organized for search results or navigation using PeopleRank of therecommenders as a factor. Another embodiment identifies subject matterexperts based on at least one of PeopleRank and interest areas. Expertsidentified in this way may be solicited to create content, comment onproposals or concepts, participate in discussions, or reviewpublications. Experts may also be selected based on location(geography).

Once experts have been identified, an embodiment of the invention trackstheir browsing activities to improve searching. Copies of pages orcontent that an expert has browsed to are stored in a search database.PeopleRank of that expert is associated with or otherwise effectivelycoupled to the page/content copy, and the copy is prioritized orotherwise ordered in the database according to the associatedPeopleRank. An embodiment includes the search database of web contentranked (prioritized or ordered) by one or more of the following factors:relevance, PeopleRank, time (of content creation). Using the browsinghistory of experts in this way results in better search results (i.e.,more relevant content) and faster discovery of online information. Anembodiment determines popular topics based on the PeopleRank ofindividuals browsing the topics. Then, popular content areas areidentified within each topic.

Alternatively, an embodiment uses PeopleRank to create a rankedcommunity of users in a computer network as described previously. Nextthe embodiment observes (tracks and monitors) those users browsingcontent/pages on various global computer network sites (which may or maynot be sites that are within the user community computer network). Theembodiment collects copies of the pages/content so browsed and storesthe collected copies in a database. The database system orders theimportance of those pages/content as a function of at least PeopleRankof the browsing user (that was monitored and caused the page/content tobe collected into the database). An improved search database (includingcontent everywhere in the global computer network) results.

In an embodiment of the invention, PeopleRank is provided as an input toan advertising targeting engine. Typically, an advertising targetingengine establishes profiles on a per-advertisement basis. The enginematches as many users as possible to a particular advertisement profilein order to determine display distribution (target audience) or ripeness(favorable time for distribution). In an embodiment of the presentinvention, advertisements are selectively displayed to a given userbased on any one or combination of the following of the user: i)PeopleRank, ii) profile, iii) interests, and iv) geography (location).Targeted advertisements may be displayed to a user on any site (e.g.,websites on the Internet) or on a telecommunications device (e.g., acomputer, PDA, and the like) capable of browsing.

In an embodiment, advertisements are bid for and bought based on thePeopleRank distribution of the intended audience. For example,advertisements are sent only to individuals above a certain PeopleRankor in a particular PeopleRank quartile, decile, or other distributioncategory.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A method for distributing digital contentcomprising: in a hardware processor: storing in a database of anInternet-based social network a plurality of user profiles, differentones of the stored user profiles representing different users in theInternet-based social network and each stored user profile includingindications of rank in the Internet-based social network of therespective user represented by the profile; accessing a search databasestoring digital content viewed by the users of the Internet-based socialnetwork, the digital content being from a plurality of global computernetwork sites; identifying an importance of an individual user as aplurality of functions of user demand of the individual user;determining at least one relative rank of the individual user based onthe identified importance, said determining including: (i) representingthe social network as an interconnected network graph having nodes anddirectional connection lines between the nodes, each node being arespective person and each directional connection line being aconnection from a person requesting the connection to a person beingconnected to, and (ii) modeling the connections between peoplerepresented in the interconnected network graph as a mathematicaledge-weighted directed graph, wherein the mathematical edge-weighteddirected graph mathematically takes into account the entireinterconnected network graph at once and derives the importance of theindividual user as the plurality of functions of user demand recursivelyfor the entire interconnected network graph, such that the at least onerelative rank of the individual user is determined after one pass ofiterations by determining a product of a first matrix and a secondmatrix, the first matrix being based on and representing connectionsbetween nodes of the mathematical edge-weighted directed graph and thesecond matrix being based on and representing a number of nodes in thegraph so as to account for the entire interconnected network graph;updating in the respective user profile, the indications of the at leastone determined relative rank of the individual user, said user profilestored in the database of user profiles of the social network;prioritizing at least some of the digital content in the search databaseas a function of at least the determined relative rank stored in theuser profiles of the respective users that viewed the digital contentand distributing the content based on the prioritization, wherein saidhardware processor is configured to perform said storing, identifying,determining, updating, prioritizing, and distributing.
 2. A method asclaimed in claim 1 wherein the plurality of functions of user demandincludes any one or more of: number of requests to be connected to theindividual user; number of actual connections made to the individualuser; number of messages sent to the individual user; number of messagessent by the individual user and opened by other users; number of publiccomments on a profile of the individual user's; number of requests toview content authored by the individual user; number of comments on theindividual user's content; number of invitations sent to the individualuser for any one or more of: i) membership to the community, ii)membership to groups, and ii) events; number of chat requests made tothe individual user; number of chat sessions engaged in by theindividual user; number of videoconference requests made to theindividual user; number of videoconference sessions engaged in by theindividual user; number of calls attempted to the individual user;number of calls received by the individual user; community membersfollowing activities of the individual user activity within the network;and keywords common between any one or combination of a profile andcontent created by a user and any one or combination of a profile andcontent of the individual user.
 3. A method as claimed in claim 2wherein the community members following may be topic specific, resultingin different ranks for each user for different topical areas.
 4. Amethod as claimed in claim 1 wherein plurality of functions of userdemand of the individual user is normalized according to a total numberof requests to be connected to any of the global computer network usersper global computer network user.
 5. A method as claimed in claim 1wherein determining the at least one rank includes recursively usingranks of people connecting to the individual user, wherein each personhas a respective rank based on his identified importance, his identifiedimportance being a function of demand for him.
 6. A method as claimed inclaim 1 further comprising, in the database, decreasing the at least onerank of the individual user if the individual user engages in behaviorthat is negative within the community.
 7. A method as claimed in claim 6further comprising establishing a watch list of individuals whoseactivities may be limited by system administrators.
 8. A method asclaimed in claim 1 further comprising decreasing the at least one rankof the individual user when connection requests sent by the individualuser to other people are declined or ignored.
 9. A method as claimed inclaim 1 further comprising sorting results of searching for members ofthe community, where the sorting includes the at least one ranks of themembers as a factor.
 10. A method as claimed in claim 1 furthercomprising sorting search results for content, where the sortingincludes the at least one ranks of authors of the content as a factor.11. A method as claimed in claim 1 further comprising: for each content,collecting information of users that browse the content includingcollecting rankings of said users; and organizing search results forcontent using a function of ranks of users browsing the content.
 12. Amethod as claimed in claim 1 further comprising: collecting userrecommendations of at least one of content and a global computer networkpage; and organizing the recommendations for at least one of searchresults and navigation using a function of ranks of users that made therecommendations.
 13. A method as claimed in claim 1 wherein members mayopt not to see an indication of a user below a certain rank.
 14. Amethod as claimed in claim 1 further comprising providing the at leastone rank of the individual user as an input into fraud detectionalgorithms.
 15. A method as claimed in claim 1 wherein the at least onerank is visible as one or more of i) a number; and ii) a tier leveldemonstrating status.
 16. A method as claimed in claim 1 wherein the atleast one rank is used in compensating the individual user forparticipation or publishing in the community.
 17. A method as claimed inclaim 1 wherein members have different privileges or technicalfunctionality based on their rank.
 18. A method as claimed in claim 1further comprising assigning a new member to the community, who isinvited to the community by another member, an initial rank upon sign-upbased on the at least one rank of the other member who invited the newmember.
 19. A method as claimed in claim 18 in which the initial rank isassigned differently to new members to the community who registerwithout being invited than to new members who are invited to join.
 20. Amethod as claimed in claim 1 further comprising providing the at leastone rank of the individual user to an entity external from thecommunity.
 21. A method as claimed in claim 20 further comprisingproviding the at least one rank to an advertising targeting engine forthe purpose of targeting advertising to people using one or more of awebsite and an identifiable telecommunications device for browsing. 22.A method as claimed in claim 1 wherein user rank in any one or more ofareas of expertise, areas of interest, and profile characteristics isused to identify a group of people to invite to do any one or more ofthe following: try a product; receive an invitation; receive a sample;receive a preview; and receive preferred status for a product.
 23. Amethod as claimed in claim 21 further comprising determiningadvertisements that will be displayed to a user based on any one or moreof user rank, profile characteristics, areas of interest, and geography.24. A method as claimed in claim 21 further comprising pricingadvertisements based on the rank distribution of people who will receivethe advertisements.
 25. A method as claimed in claim 1 furthercomprising identifying experts to do any one or more of creatingcontent, commenting on proposals, participating in discussion, andreviewing publications; wherein the identifying is based on any one ormore of user rank, profile characteristics, and areas of interest.
 26. Amethod as claimed in claim 25 wherein the identifying is based ongeography to identify experts in a specific location.
 27. A computersystem for distributing digital content comprising: a database of anInternet-based social network containing a plurality of user profiles,different ones of the stored user profiles representing different usersin the Internet-based social network, each stored user profile includingindications of rank in the Internet-based social network of therespective user represented by the profile; a search database containingdigital content viewed by the users of the Internet-based socialnetwork, the digital content being from a plurality of global computernetwork sites; a processor configured to identify an importance of theindividual user as a plurality of functions of user demand; theprocessor further configured to execute a computer program comprisinginstructions for execution by a processor, from a non-transitorycomputer readable storage medium, the computer program adapted for:determining at least one relative rank of the individual user based onthe identified importance, said determining including: (i) representingthe social network as an interconnected network graph having nodes anddirectional connection lines between the nodes, each node being arespective person and each directional connection line being aconnection from a person requesting the connection to a person beingconnected to, and (ii) modeling the connections between peoplerepresented in the interconnected network graph as a mathematicaledge-weighted directed graph, wherein the mathematical edge-weighteddirected graph mathematically takes into account the entireinterconnected network graph at once and derives the importance of theindividual user as the plurality of functions of user demand recursivelyfor the entire interconnected network graph, such that the at least onerelative rank of the individual user is determined after one pass ofiterations by determining a product of a first matrix and a secondmatrix, the first matrix being based on and representing connectionsbetween nodes of the mathematical edge-weighted directed graph and thesecond matrix being based on and representing a number of nodes in thegraph so as to account for the entire interconnected network graph; andupdating in the user profile, the indications of the at least onedetermined relative rank of the individual user, said user profilecorresponding to the individual user and being stored in the database ofuser profiles of the social network; prioritizing at least some of thedigital content in the search database as a function of at least thedetermined relative rank stored in the user profiles of the respectiveusers that viewed the digital content and distributing the digitalcontent at least based on the prioritization.
 28. A computer system asclaimed in claim 27 wherein the plurality of functions of user demandincludes any one or more of: number of requests to be connected to theindividual user; number of actual connections made to the individualuser; number of messages sent to the individual user; number of messagessent by the individual user and opened by other users; number of publiccomments on a profile of the individual user's; number of requests toview content authored by the individual user; number of comments on theindividual user's content; number of invitations sent to the individualuser for any one or more of: i) membership to the community, ii)membership to groups, and ii) events; number of chat requests made tothe individual user; number of chat sessions engaged in by theindividual user; number of videoconference requests made to theindividual user; number of videoconference sessions engaged in by theindividual user; number of calls attempted to the individual user;number of calls received by the individual user; community membersfollowing activities of the individual user activity within the network;and keywords common between any one or combination of a profile andcontent created by a user and any one or combination of a profile andcontent of the individual user.
 29. A computer system as claimed inclaim 28 wherein the community members following may be topic specific,resulting in different ranks for each user for different topical areas.30. A computer system as claimed in claim 27 wherein the plurality offunctions of user demand is normalized according to a total number ofrequests to be connected to any of the global computer network users perglobal computer network user.
 31. A computer system as claimed in claim27 wherein determining the at least one rank includes recursively usingranks of people connecting to the individual user, wherein each personhas a respective rank based on his identified importance, his identifiedimportance being a function of demand for him.
 32. A computer system asclaimed in claim 27 wherein the processor is further configured todecrease, in the database, the at least one rank of the individual userif the individual user engages in behavior that is negative within thecommunity.
 33. A computer system as claimed in claim 32 furthercomprising a watch list of individuals whose activities may be limitedby system administrators.
 34. A computer system as claimed in claim 27wherein the processor is further configured to decrease the at least onerank of the individual user when connection requests sent by theindividual user to other people are declined or ignored.
 35. A computersystem as claimed in claim 27 wherein the processor is furtherconfigured to sort results of searching for members of the community,where the sorting includes the ranks of the members as a factor.
 36. Acomputer system as claimed in claim 27 wherein the processor is furtherconfigured to sort search results for content, where the sortingincludes the ranks of authors of the content as a factor.
 37. A computersystem as claimed in claim 27 wherein the processor is furtherconfigured to: collect information of users who are browsing content,including collecting ranks of said users; and organize search resultsfor content using a function of ranks of users browsing the content. 38.A computer system as claimed in claim 27 wherein members may opt not tosee an indication of a user below a certain rank.
 39. A computer systemas claimed in claim 27 wherein the processor is further configured toprovide the at least one rank of the individual user as an input intofraud detection system.
 40. A computer system as claimed in claim 27wherein the at least one rank is visible as one or more of i) a number;and ii) a tier level demonstrating status.
 41. A computer system asclaimed in claim 27 wherein the at least one rank is used incompensating the individual user for participation or publishing in thecommunity.
 42. A computer system as claimed in claim 27 wherein membershave different privileges or technical functionality based on theirrank.
 43. A computer system as claimed in claim 27 wherein the processoris further configured to assign a new member to the community, who isinvited to the community by another member, an initial rank upon sign-upbased on the at least one rank of the other member who invited the newmember.
 44. A computer system as claimed in claim 43 in which theinitial rank is assigned differently to new members to the community whoregister without being invited than to new members who are invited tojoin.
 45. A computer system as claimed in claim 27 wherein the processoris further configured to provide the at least one rank of the individualuser to an entity external from the community.
 46. A computer system asclaimed in claim 27 wherein user rank in any one or more of areas ofexpertise, areas of interest, and profile characteristics is used toidentify a group of people to invite to do any one or more of thefollowing: try a product; receive an invitation; receive a sample;receive a preview; and receive preferred status for a product.
 47. Acomputer system as claimed in claim 27 wherein the processor is furtherconfigured to identify experts to do any one or more of creatingcontent, commenting on proposals, participating in discussion, andreviewing publications; wherein the identifying is based on any one ormore of user rank, profile characteristics, and areas of interest.
 48. Acomputer system as claimed in claim 47 wherein the identifying is basedon geography to identify experts in a specific location.